Design by space transformation form high to low dimensions

Abstract

A method is provided for transforming data from a high-dimensional to low-dimensional design space, and for inspecting the transformed data in such a manner as to permit effective navigation and exploration of the high-dimensional design space. Conveniently, an optimum / conditional value for a prescribed functional representation of the transformed data can be derived by visual inspection of a 2-D image map representation of the transformed data. Significantly, the invention has utility for various aircraft design applications and although this technology has been developed with reference to aircraft aerodynamic design in particular, the design space visualisation and curve-fitting technology developed is general. It should therefore be equally applicable to other disciplines such as cost analysis, structures and computational electromagnetics, in which expensive analysis tools are used to find optima for complicated design problems. It is expected to be particularly useful in multi-disciplinary design and situations where there are multiple optima in the design space.

Description

FIELD OF THE INVENTION [0001] This invention concerns design by space transformation from high to low dimensions. More particularly but not exclusively, this invention concerns methods and systems for design by transforming data from a high to low dimensional design space. BACKGROUND OF THE INVENTION [0002] There are considerable cost benefits to be derived from improving designs both by better searches in current design spaces and by satisfying more complex design criteria, for instance by striking the right balance between multiple objectives from the same discipline or from different disciplines. [0003] In known design processes designers typically spend a prohibitively long time preparing individual analysis cases and, are constrained to sampling small areas of the design space close to previously explored regions. In such a design processes, complex design tasks are broken down into manageable portions so that specialists in individual disciplines can resolve different parts of...

AI Technical Summary

Benefits of technology

[0005] It is an object of the present invention to provide a method and system which are reliable for providing enhanced comprehension and effective navigation of multi-dimensional design spaces.

[0006] In broad terms, the present invention resides in the concept of transforming data from a high-dimensional to low-dimensional design space and, by inspecting the transformed data in the low-dimensional design space, enabling an optimum value for a functional representation of the transformed data to be established, and thereby permitting effective exploration of the high-dimensional design space.

[0012] In accordance with another embodiment of the invention which will be described hereinafter in detail, the method comprises the step of transforming data from a first high-dimensional design space and from a second, different high-dimensional design space into a low-dimensional design space, comparing the different transformed data sets in said low-dimensional design space and identifying therefrom similarities between the different transformed data sets to indicate a correspondence between the first and second high-dimensional design spaces. This provides an effective way of assessing the importance and inter relationship of design variables in different high-dimensional design space problems. In this embodiment, to be described hereafter, the first high-dimensional design space is a 5-dimensional design space, the second high-dimensional design space is an 8-dimensional design space, the third high-dimensional design space is a 14-dimensional design space, and the low-dimensional design is a two-dimensional design space. It is to be appreciated, however, that various other kinds of high-dimensionality design space can be readily accommodated, if desired.

Problems solved by technology

In known design processes designers typically spend a prohibitively long time preparing individual analysis cases and, are constrained to sampling small areas of the design space close to previously explored regions.

However, such portions are too small and there is too much iteration between disciplines which prohibits the challenging of the constraints of one discipline by another, where the most progress is likely to be made.

Furthermore, known design space exploration techniques as applied to complex multi-dimensional design spaces are invariably inefficient on account of the associated high computational processing cost required for the data analysis.

Also, such techniques can be problematic to use in complex multi-dimensional design spaces because these typically rely on simplistic data analysis models involving different parameters—for example, numerical errors or uncertainties associated with discretisation, incomplete convergence, irregular constraint boundaries can arise.

They can use software and data as web services, so that the source of this data and software becomes unknown and unimportant.

Such designers make use of high cost analysis codes within automated search and response surface models to map and optimise domains using computationally expensive evaluations.

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